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http://dx.doi.org/10.6109/jkiice.2014.18.7.1531

PVC Classification by Personalized Abnormal Signal Detection and QRS Pattern Variability  

Cho, Ik-Sung (Department of Information and Communication Engineering, Kyungwoon University)
Yoon, Jeong-Oh (Department of Information and Communication Engineering, Kyungwoon University)
Kwon, Hyeog-Soong (Department of IT Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.18, no.7, 2014 , pp. 1531-1539 More about this Journal
Abstract
Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. In other words, the design of algorithm that exactly detects abnormal signal and classifies PVC by analyzing the persons's physical condition and/or environment and variable QRS pattern is needed. Thus, PVC classification by personalized abnormal signal detection and QRS pattern variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and subtractive operation method and selected abnormal signal sets. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of abnormal beat detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 98.33% in abnormal beat classification error and 94.46% in PVC classification.
Keywords
Abnormal Signal; QRS pattern; PVC; R wave amplitude;
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Times Cited By KSCI : 2  (Citation Analysis)
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